ArticleJournal of computer-aided molecular design2021
CAVIAR: a method for automatic cavity detection, description and decomposition into subcavities.
Article in Journal of computer-aided molecular design, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed, 34 citations in OpenAlex.
- Can Cavity Prediction Algorithms Help in Docking Experiments?Journal of computational chemistry · 2026Article
- Cheminformatic analysis of protein surfaces provides binding site insights and informs identification strategies.Future medicinal chemistry · 2025Article
- GENEOnet: a breakthrough in protein binding pocket detection using group equivariant non-expansive operators.Scientific reports · 2025Article
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- Activation of the bacterial defense-associated sirtuin system.Communications biology · 2025Article
- Flavonoids as Potential Modulators of Pancreatic Lipase Catalytic Activity.Pharmaceutics · 2025Article
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- Quantitative Characterization of the Impact of Protein-Protein Interactions on Ligand-Protein Binding: A Multi-Chain Dynamics Perturbation Analysis Method.International journal of molecular sciences · 2024Article
- A comprehensive exploration of the druggable conformational space of protein kinases using AI-predicted structures.PLoS computational biology · 2024Article
- Targeting CDK9 in Cancer: An Integrated Approach of Combining In Silico Screening with Experimental Validation for Novel Degraders.Current issues in molecular biology · 2024Article
- CRAFT: a web-integrated cavity prediction tool based on flow transfer algorithm.Journal of cheminformatics · 2024Article
- 1,3,4-oxadiazoles as inhibitors of the atypical member of the BET family bromodomain factor 3 fromFrontiers in microbiology · 2024Article
- SiteFerret: Beyond Simple Pocket Identification in Proteins.Journal of chemical theory and computation · 2023Article
- A multilayer dynamic perturbation analysis method for predicting ligand-protein interactions.BMC bioinformatics · 2022Article
- Estimating the Similarity between Protein Pockets.International journal of molecular sciences · 2022Review
- A Molecular Docking Study of Human STEAP2 for the Discovery of New Potential Anti-Prostate Cancer Chemotherapeutic Candidates.Frontiers in bioinformatics · 2022Article
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Authors and funding
4 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
The accurate description of protein binding sites is essential to the determination of similarity and the application of machine learning methods to relate the binding sites to observed functions. This work describes CAVIAR, a new open source tool for generating descriptors for binding sites, using protein structures in PDB and mmCIF format as well as trajectory frames from molecular dynamics simulations as input. The applicability of CAVIAR descriptors is showcased by computing machine learning predictions of binding site ligandability. The method can also automatically assign subcavities, even in the absence of a bound ligand. The defined subpockets mimic the empirical definitions used in medicinal chemistry projects. It is shown that the experimental binding affinity scales relatively well with the number of subcavities filled by the ligand, with compounds binding to more than three subcavities having nanomolar or better affinities to the target. The CAVIAR descriptors and methods can be used in any machine learning-based investigations of problems involving binding sites, from protein engineering to hit identification. The full software code is available on GitHub and a conda package is hosted on Anaconda cloud.
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